Introduction

This report analyzes Connecticut county-level home values for single-family homes and condos, comparing pre-, during-, and post-COVID periods (2018–2025). Data is from Zillow’s ZHVI dataset.

Data Source: https://www.zillow.com/research/data

Note: This data was downloaded on June 27, 2025, so average 2025 home values reflect figures up to that date.

📁 Load and Filter Data

# Read in the Zillow Home Value Index (ZHVI) datasets for Single Family and Condo homes
sfr_frame <- read.csv("County_zhvi_uc_sfr_tier_0.33_0.67_sm_sa_month.csv") 
condo_frame <- read.csv("County_zhvi_uc_condo_tier_0.33_0.67_sm_sa_month.csv")

# Filter data to only include Connecticut counties
sfr_frame <- sfr_frame %>% filter(State == "CT")
condo_frame <- condo_frame %>% filter(State == "CT")

📊 Calculate Yearly Averages

# Create new columns for each year by averaging monthly values
# Use [[ ]] to dynamically assign to columns named "2018", "2019", ..., "2025"
years <- 2018:2025

for(year in years) {
  sfr_frame[[as.character(year)]] <- rowMeans(select(sfr_frame, contains(paste0("X", year))))
  condo_frame[[as.character(year)]] <- rowMeans(select(condo_frame, contains(paste0("X", year)))) 
}

# Keep only relevant columns for analysis
sfr_frame <- sfr_frame %>% select(State, RegionName, MunicipalCodeFIPS, all_of(as.character(years)))
condo_frame <- condo_frame %>% select(State, RegionName, MunicipalCodeFIPS, all_of(as.character(years)))

📈 Trend Over Time – Single-Family Residences

# Convert data to long format for plotting
sfr_frame_long <- sfr_frame %>% 
  pivot_longer(cols = contains("20"), names_to = "Year", values_to = "Average_Home_Values") %>%
  mutate(Year = factor(Year,levels = as.character(2018:2025))) %>% # Factored to preserve correct order in plot
  arrange(RegionName, Year) 

# Trend over time plot
ggplot( sfr_frame_long, aes(x=Year, y=Average_Home_Values, colour = RegionName, group = RegionName)) +
         geom_line() + 
         geom_point() +
         labs(title = "Connecticut Single-Family Home Values: Pre, During and Post-COVID", x= "Year", y = "Avg. Single Family Home Value ($)", colour= "County") +
         scale_y_continuous(labels = label_comma()) + # Format Y axis with commas - Not Scientific Notation
         geom_vline(xintercept = which(levels(sfr_frame_long$Year) %in% c("2020", "2023")), linetype= "dashed", colour= "gray40") + # Mark start/end of COVID period
         annotate("text", x=1.5, y=Inf, label= "Pre-COVID", vjust= 1.5, size = 4) + 
         annotate("text", x=4.5, y=Inf, label= "During COVID", vjust= 1.5, size = 4) +
         annotate("text", x=7.25, y=Inf, label= "Post-COVID", vjust= 1.5, size = 4) +
         theme(plot.title = element_text(hjust = 0.5, size = 14))

Single-Family Trends Plot
* Fairfield County stands out, with 2025 average home values almost $300,000 higher than any other county in Connecticut.
* Home values rose steadily across all counties from 2020 to 2025.

📋 Table – Percent Increase in Single-Family Values

# Calculate percent increase from 2018 to 2025 for SFR
Home_Value_Percent_Increase_Table <- sfr_frame %>%
  mutate(Delta_Price = `2025` - `2018`,
         Percent_Increase = round(((`2025` - `2018`) / `2018`) * 100)) %>%
  select(RegionName,`2018`, `2025`, Delta_Price, Percent_Increase) %>%
  arrange(-Percent_Increase) #Sort by highest increase

# Display the result in a formulated table using gt
Home_Value_Percent_Increase_Table %>%
  gt() %>%
  cols_align(align = "center", columns = everything()) %>%
  cols_label(
    `2018` = md("**2018 Avg. Home Price ($)**"),
    `2025` = md("**2025 Avg. Home Price ($)**"),
    Delta_Price = md("**Price Increase ($)**"),
    RegionName = md("**County**"),
    Percent_Increase = md("**Percent Increase (%)**")
  ) %>%
  tab_header(title = md("**Percent Increase in Average Single-Family Home Prices (2018-2025)**")) %>%
  tab_style(
    style = cell_borders(sides = "all", color = "black", weight = px(1.5)),
    locations=cells_body()
  )
Percent Increase in Average Single-Family Home Prices (2018-2025)
County 2018 Avg. Home Price ($) 2025 Avg. Home Price ($) Price Increase ($) Percent Increase (%)
Litchfield County 254520.3 435067.0 180546.7 71
Windham County 211888.5 362937.8 151049.3 71
New Haven County 245673.2 414148.3 168475.1 69
New London County 248132.2 414567.9 166435.7 67
Middlesex County 282020.0 460935.3 178915.3 63
Hartford County 243776.3 394179.4 150403.1 62
Tolland County 246818.1 398353.5 151535.4 61
Fairfield County 486487.4 748105.7 261618.3 54

Single-Family Percent Increase Table
* Litchfield & Windham county led with a 71% increase, followed by New Haven at 69%.
* Every county gained over $150,000 in average home value between 2018 and 2025.

📊 Bar Chart – Single-Family Home Value Increase

# Long format for chart
sfr_long <- Home_Value_Percent_Increase_Table %>%
  pivot_longer(cols = c(`2018`, `2025`), names_to = "Year", values_to = "Average_Home_Values") %>%
  mutate(
    Bar_Chart_Format_Col = case_when(
      Year == "2025" ~ paste0("$", round(Average_Home_Values), " <br> ", "(", Percent_Increase, "%)"),
      Year == "2018" ~ paste0("$", round(Average_Home_Values))
    )
  )

# Bar chart
ggplot(sfr_long, aes(x= reorder(RegionName,-Percent_Increase), y=Average_Home_Values, fill = Year)) + 
  geom_col(position = position_dodge(width = 0.9)) + 
  labs(title = "Change in Single Family Home Values by County (2018 to 2025)",
    x= "County",
    y= "Home Value ($)",
    caption = "Note: 2025 bars show dollar value and percent increase (in parentheses)") +
  scale_y_continuous(labels = label_comma(), expand = expansion(mult = c(0, 0.2))) + #expanded y axis to include labels
  scale_fill_manual(values = c("2018" = "#552448", "2025"= "#eae3d4")) +
  ggtext::geom_richtext(
    aes(label = Bar_Chart_Format_Col, group = Year),
    position = position_dodge(width = 0.9), vjust= 0, size = 3,
    fill = NA, label.color = NA
  ) + 
  theme_minimal() +
  theme(
    axis.text.x = element_text(angle=45, hjust= 1),
    plot.title = element_text(hjust = 0.5) # center plot title
  )

Single-Family Bar Chart
* All counties showed growth above 50%, indicating broad-based appreciation.
* Fairfield County average home values in 2025 have increased the most since 2018 with an increase of $262,000.

📈 Trend Over Time – Condos

# Convert data to long format for plotting
condo_frame_long <- condo_frame %>% 
  pivot_longer(cols = contains("20"), names_to = "Year", values_to = "Average_Home_Values") %>%
  mutate(Year = factor(Year,levels = as.character(2018:2025))) %>% # Factored to preserve correct order in plot
  arrange(RegionName, Year)

# Trend over time plot
ggplot( condo_frame_long, aes(x=Year, y=Average_Home_Values, colour = RegionName, group = RegionName)) +
         geom_line() + 
         geom_point() +
         labs(title = "Connecticut Condo Values: Pre, During and Post-COVID", x= "Year", y = "Avg. Condo Value ($)", colour= "County") +
         scale_y_continuous(labels = label_comma()) + # Format Y axis with commas - not scientific notation
         geom_vline(xintercept = which(levels(sfr_frame_long$Year) %in% c("2020", "2023")), linetype= "dashed", colour= "gray40") + # Mark start/end of COVID period
         annotate("text", x=1.5, y=Inf, label= "Pre-COVID", vjust= 1.5, size = 4) + 
         annotate("text", x=4.5, y=Inf, label= "During COVID", vjust= 1.5, size = 4) +
         annotate("text", x=7.25, y=Inf, label= "Post-COVID", vjust= 1.5, size = 4) +
         theme(plot.title = element_text(hjust = 0.5, size = 16))

Condo Trends Plot
* Again Fairfield County stands out, with 2025 average condo values over $130,000 higher than any other county in Connecticut.
* Average condo values rose sharply from 2020 to 2025.

📋 Table – Condo Price Increase

# Calculate percent increase from 2018 to 2025 for condos
Condo_Value_Percent_Increase_Table <- condo_frame %>%
  mutate(Delta_Price = `2025` - `2018`,
         Percent_Increase = round(((`2025` - `2018`) / `2018`) * 100)) %>%
  select(RegionName,`2018`, `2025`, Delta_Price, Percent_Increase) %>%
  arrange(-Percent_Increase) #Sort by highest increase

# Display the result in a formulated table using gt
  gt(Condo_Value_Percent_Increase_Table) %>%
  cols_align(align = "center", columns = everything()) %>%
  cols_label(
    `2018` = md("**2018 Avg. Condo Price ($)**"),
    `2025` = md("**2025 Avg. Condo Price ($)**"),
    Delta_Price = md("**Price Increase ($)**"),
    RegionName = md("**County**"),
    Percent_Increase = md("**Percent Increase (%)**")
  ) %>%
  tab_header(title = md("**Condo Value Increase (2018-2025)**")) %>%
  tab_style(
    style = cell_borders(sides = "all", color = "black", weight = px(1.5)),
    locations=cells_body()
  )
Condo Value Increase (2018-2025)
County 2018 Avg. Condo Price ($) 2025 Avg. Condo Price ($) Price Increase ($) Percent Increase (%)
Litchfield County 123213.2 239816.0 116602.79 95
New Haven County 138569.3 261619.0 123049.69 89
Windham County 146171.8 265885.5 119713.69 82
New London County 143628.0 257907.9 114279.83 80
Hartford County 148098.9 256681.7 108582.77 73
Tolland County 146908.7 253386.0 106477.32 72
Middlesex County 153875.4 251431.3 97555.95 63
Fairfield County 262862.2 397200.6 134338.36 51

Condo Value Increase Table
* Litchfield County saw the largest increase in condo values, up 95% since 2018.
* New Haven followed closely with a 89% increase.
* Most counties gained over $100,000 in average condo value between 2018 and 2025.

📊 Bar Chart – Condo Price Increase

# Long format for chart
condo_long <- Condo_Value_Percent_Increase_Table %>%
  pivot_longer(cols = c(`2018`, `2025`), names_to = "Year", values_to = "Average_Condo_Values") %>%
  mutate(
    Bar_Chart_Format_Col = case_when(
      Year == "2025" ~ paste0("$", round(Average_Condo_Values), " <br> ", "(", Percent_Increase, "%)"),
      Year == "2018" ~ paste0("$", round(Average_Condo_Values))
    )
  )

#Bar chart
ggplot(condo_long, aes(x= reorder(RegionName,-Percent_Increase), y=Average_Condo_Values, fill = Year)) + 
  geom_col(position = position_dodge(width = 0.9)) + 
  labs(
    title = "Change in Condo Prices by County (2018 to 2025)",
    x= "County",
    y= "Condo Value ($)",
    caption = "Note: 2025 bars show dollar value and percent increase (in parentheses)") +
  scale_y_continuous(labels = label_comma(), expand = expansion(mult = c(0, 0.2))) + #expanded y axis to include labels
  scale_fill_manual(values = c("2018" = "#6fa8dd", "2025"= "#cccccc")) +
  ggtext::geom_richtext(
    aes(label = Bar_Chart_Format_Col, group = Year),
    position = position_dodge(width = 0.9), vjust= 0, size = 2.5,
    fill = NA, label.color = NA
  ) + 
  theme_minimal() +
  theme(
    axis.text.x = element_text(angle=45, hjust= 1),
    plot.title = element_text(hjust = 0.5) # center plot title
  )

Condo Bar Chart
* Fairfield county average home values in 2025 have increased the most since 2018 with an increase of $134,000.
* All counties showed growth above 50%, indicating broad-based appreciation.
* Counties with lower 2018 prices (e.g., Litchfield, New Haven) saw the largest percentage increases.

🏠📈 Condo vs. Single-Family Home Value Growth Comparison

# Join sfr and condo tables
condo_vs_sfr <- Home_Value_Percent_Increase_Table %>% 
  left_join(Condo_Value_Percent_Increase_Table, by= "RegionName") %>% 
  select(RegionName, Percent_Increase.x, Percent_Increase.y)

# Rename columns
condo_vs_sfr <- condo_vs_sfr %>%
  rename(County = RegionName, SFR = Percent_Increase.x, Condo = Percent_Increase.y)

# Long format for bar chart
condo_vs_sfr_Percent_Increase_long <- condo_vs_sfr %>% pivot_longer(cols = contains(c("SFR","Condo")), names_to = "Home_Type", values_to = "Percent_Increase")

# Comparison bar chart
condo_vs_sfr_Percent_Increase_long %>% ggplot(aes(x= reorder(County,-Percent_Increase), y=Percent_Increase, fill = Home_Type)) + 
  geom_col(position = position_dodge(width = 0.9)) + 
  labs(title = "Condo vs. Single-Family: Price Increase (2018-2025)",
       x= "County",
       y= "Percent Increase (%)") +
  scale_y_continuous(labels = label_comma()) +
  scale_fill_manual(values = c("SFR" = "#eae3d4", "Condo" = "#cccccc")) +
  ggtext::geom_richtext(
    aes(label = paste0(Percent_Increase,"%"), group = Home_Type),
    position = position_dodge(width = 0.9), vjust= 0, size = 3,
    fill = NA, label.color = NA
  ) + 
  theme_minimal() +
  theme(
    axis.text.x = element_text(angle=45, hjust= 1),
    plot.title = element_text(hjust = 0.5) # center plot title
  )

Condo vs. Single-Family Comparison Bar Chart
* For most Connecticut counties, condos outpaced single-family homes in percentage growth.
* The largest growth gaps appeared in counties like Litchfield and New Haven,
where condos outperformed single family residences (SFR) by 20+ percentage points.
* This highlights affordability-driven demand and shifting buyer behavior post-COVID.

📌 Summary Statistics (2018–2025)

# Summary statistics for Single-Family Residences
sfr_summary <- Home_Value_Percent_Increase_Table %>%
  summarise(
    Mean_2018 = mean(`2018`),
    Mean_2025 = mean(`2025`),
    Avg_Percent_Increase = round(mean(Percent_Increase), 0),
    Max_Percent_Increase = max(Percent_Increase),
    Min_Percent_Increase = min(Percent_Increase)
  )
# Summary statistics for Condos
condo_summary <- Condo_Value_Percent_Increase_Table %>%
  summarise(
    Mean_2018 = mean(`2018`),
    Mean_2025 = mean(`2025`),
    Avg_Percent_Increase = round(mean(Percent_Increase), 0),
    Max_Percent_Increase = max(Percent_Increase),
    Min_Percent_Increase = min(Percent_Increase)
  )

# Combine for comparison
summary_stats <- bind_rows( # Binds sfr and condo summaries together and adds a column of Home_Type
  sfr_summary %>% mutate(Home_Type = "Single-Family"),
  condo_summary %>% mutate(Home_Type = "Condo")
  ) %>%
  select(Home_Type, everything()) %>% #selects to put Home_Type first
  mutate(
    Mean_2018 = dollar(Mean_2018),# adds $ in front
    Mean_2025 = dollar(Mean_2025),
    Avg_Percent_Increase = paste0(Avg_Percent_Increase, "%"), #adding % in back
    Max_Percent_Increase = paste0(Max_Percent_Increase, "%"),
    Min_Percent_Increase = paste0(Min_Percent_Increase, "%")
  )
  
# Display as a table
gt(summary_stats) %>%
  cols_label(
    Home_Type = "Home Type",
    Mean_2018 = "Avg. Value (2018)",
    Mean_2025 = "Avg. Value (2025)",
    Avg_Percent_Increase = "Mean % Increase",
    Max_Percent_Increase = "Max % Increase",
    Min_Percent_Increase = "Min % Increase"
    ) %>%
  tab_header(
    title = md("**Summary Statistics of Home Value Changes (2018–2025)**")
  ) %>%
  cols_align(align = "center", columns = everything()) %>%
  tab_style(
    style = cell_borders(sides = "all", color = "black", weight = px(1.5)),
    locations=cells_body()
  )
Summary Statistics of Home Value Changes (2018–2025)
Home Type Avg. Value (2018) Avg. Value (2025) Mean % Increase Max % Increase Min % Increase
Single-Family $277,415 $453,537 65% 71% 54%
Condo $157,916 $272,991 76% 95% 51%

🧾 Conclusion

Between 2018 and 2025, Connecticut’s housing market experienced strong appreciation across both single-family homes and condos, with condos slightly outpacing single-family homes in average percentage growth.

🏡 Single-Family Residences

  • Average value rose from $277,415 to $453,537, a 65% increase statewide.
  • Growth ranged from 54% to 71%, showing relatively consistent appreciation across counties.
  • This trend suggests continued demand for suburban and rural living, likely accelerated by COVID-era preferences for more space and lower-density environments.

🏢 Condominiums

  • Average value increased from $157,916 to $272,991, a 76% increase overall.
  • Growth varied more widely—51% to 95%, depending on county.
  • Higher average growth may reflect rising condo appeal due to affordability constraints, changing lifestyle needs, and more first-time buyers or downsizers entering the market.

📌 Final Insights

  • Condos outpaced single-family homes in percent growth (76% vs. 65%), despite starting from a lower value base.
  • The COVID period (2020–2023) marked a clear inflection point for home value acceleration.
  • Some counties saw condo values nearly double, with a max growth of 95%.

These findings highlight a resilient and evolving housing market in Connecticut. For investors, planners, and homeowners, understanding these dynamics is essential for future real estate strategy and decision-making.